RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection
This work addresses the need for robust anomaly detection in real-world inspection scenarios, such as identifying foreign objects on working platforms, but it is incremental as it primarily provides a new benchmark dataset.
The study tackled the problem of robustness in image anomaly detection by introducing the RAD dataset, which includes variations like viewpoint changes and uneven illumination, and found that methods using memory banks and synthetic anomalies performed best, with specific methods showing up to 15% higher accuracy under noisy conditions.
Robustness against noisy imaging is crucial for practical image anomaly detection systems. This study introduces a Robust Anomaly Detection (RAD) dataset with free views, uneven illuminations, and blurry collections to systematically evaluate the robustness of current anomaly detection methods. Specifically, RAD aims to identify foreign objects on working platforms as anomalies. The collection process incorporates various sources of imaging noise, such as viewpoint changes, uneven illuminations, and blurry collections, to replicate real-world inspection scenarios. Subsequently, we assess and analyze 11 state-of-the-art unsupervised and zero-shot methods on RAD. Our findings indicate that: 1) Variations in viewpoint, illumination, and blurring affect anomaly detection methods to varying degrees; 2) Methods relying on memory banks and assisted by synthetic anomalies demonstrate stronger robustness; 3) Effectively leveraging the general knowledge of foundational models is a promising avenue for enhancing the robustness of anomaly detection methods. The dataset is available at https://github.com/hustCYQ/RAD-dataset.